Bottom Line:
Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery.We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD).The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.

ABSTRACTThis paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.

Mentions:
All of the 7,055 entities belong to exactly one component and bi-component. This means that the network is well connected, as at least two distinct semi-paths connect every pair of entities. The density of a network shows the degree of connections between any given two pairs in this network. If it is applied to data with values, density shows the average strength of the ties across all possible ties. The density of the Metformin network is 0.005311, which means that 0.5311% of all possible connections are presented in the current network. According to the K-core analysis, the biggest k-core (188-core) consist of 238 entities, which means that each entity in this sub-network has a connection with at least 188 others. The mean geodesic distance is 2.10. This means the average of shortest path between any two nodes is about two nodes long (not including the two given nodes). Therefore, information can be transferred efficiently through this network. The diameter (e.g., the largest geodesic distance between nodes pairs in the network) is four; between GENE otc and GENE ube2v1.This indicates that there is a close relation among all the entities, as every pair of entities could be reached by one another within three steps. Figure 5 shows the longest path (e.g., the diameter of the network) from Gene otc to Gene ube2v1.

Mentions:
All of the 7,055 entities belong to exactly one component and bi-component. This means that the network is well connected, as at least two distinct semi-paths connect every pair of entities. The density of a network shows the degree of connections between any given two pairs in this network. If it is applied to data with values, density shows the average strength of the ties across all possible ties. The density of the Metformin network is 0.005311, which means that 0.5311% of all possible connections are presented in the current network. According to the K-core analysis, the biggest k-core (188-core) consist of 238 entities, which means that each entity in this sub-network has a connection with at least 188 others. The mean geodesic distance is 2.10. This means the average of shortest path between any two nodes is about two nodes long (not including the two given nodes). Therefore, information can be transferred efficiently through this network. The diameter (e.g., the largest geodesic distance between nodes pairs in the network) is four; between GENE otc and GENE ube2v1.This indicates that there is a close relation among all the entities, as every pair of entities could be reached by one another within three steps. Figure 5 shows the longest path (e.g., the diameter of the network) from Gene otc to Gene ube2v1.

Bottom Line:
Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery.We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD).The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.

ABSTRACTThis paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.